Details For Datasets. \( \) 
The unified auto-encoder model was trained as I-frame compression on 30K color PNG images with high resolution, scraped from Flickr [1]}, and CLIC training dataset [2]}. The Vimeo-90k dataset [3]} is a widely used dataset for low-level vision tasks and has been applied to learned video compression tasks recently, so we used it for the P-frame training. To evaluate the compression performance and compare it with other methods, we employed three datasets with the resolution of 1080P (1920x1080).
HEVC common test sequences [4]} are the most popular test sequences for evaluating video compression performance, in which the contents are diversified and challenging. For a regular comparison with [5]}, [6]}, We used Class B (1080P) in experiments.
Ultra Video Group (UVG) dataset [7]} is composed of 16 versatile test video sequences captured at 120 fps, in which the motion between adjacent frames is small.
MCL-JVC dataset [8]} consists of 30 movie clips with a variety of scenarios, which contains less noise and has been used for video quality assessment [9]}.
